load complete adata from previous run
load missing intermediate adata from previous run
Extract count data..
Filter genes
(Downsample cells to make things easier)
Normalize for dimensional reduction
## Warning in if (!class(counts) %in% c("dgCMatrix", "dgTMatrix")) {: the condition
## has length > 1 and only the first element will be used
## Converting to sparse matrix ...
Dimensional reduction
Run velocyto on panc data
## Warning in if (!class(counts) %in% c("dgCMatrix", "dgTMatrix")) {: the condition
## has length > 1 and only the first element will be used
## Converting to sparse matrix ...
Scores of observed and projected states in PC space
Graph visualization on subset of cells from PC coordinates
Extract count data..
Filter genes
(Downsample cells to make things easier)
Normalize for dimensional reduction
## Warning in if (!class(counts) %in% c("dgCMatrix", "dgTMatrix")) {: the condition
## has length > 1 and only the first element will be used
## Converting to sparse matrix ...
Dimensional reduction
Run velocyto on panc data
## Warning in if (!class(counts) %in% c("dgCMatrix", "dgTMatrix")) {: the condition
## has length > 1 and only the first element will be used
## Converting to sparse matrix ...
Scores of observed and projected states in PC space
Graph visualization from PC coordinates
Above, I used k=30 for direct comparison to cellRank graph which computes distances to K=30 nearest neighbors uses. However, this might not be where veloviz performs best.
Compare mean distance between cells before and after the gap normalized by max distance between any two cells for each graph.